Starting today, Reddit rolled out a series of updates aimed at making it easier for small and medium-sized businesses (SMBs) to advertise on the platform.
The changes focus on simplifying the ad creation process, improving signal quality, and helping advertisers move campaigns from other platforms like Meta with fewer headaches.
These updates follow Reddit’s continued push to make its Ads Manager more accessible, especially for smaller businesses that may not have the luxury of dedicated ad ops teams or outside agencies.
Launching Campaigns Faster With New Tools
In the Reddit Ads update, they announced two new tools to streamline campaign creation:
Campaign Import.
Simplified Campaign Quality Assurance (QA).
The first of the additions is Campaign Import, a tool that lets advertisers bring campaigns over from Meta directly into Reddit Ads Manager.
The process is straightforward — after connecting their Meta account, advertisers can select an existing campaign, import it, and make any necessary adjustments to suit Reddit’s environment.
This isn’t just a time-saver; it gives brands a quick way to leverage proven creative and targeting strategies while adapting them to Reddit’s unique audiences.
Another welcomed update is Reddit’s new Campaign Quality Assurance (QA) system. Instead of clicking back and forth between settings pages, advertisers now get a consolidated review page summarizing all key campaign details.
If something looks off — budget, targeting, placements, or creative — users can jump directly to the relevant section and make fixes before going live.
It may seem small, but anyone who’s fumbled through nested ad platforms under tight deadlines knows how much this improves workflow.
Improved Quality Signals In Reddit Ads
In addition to the streamlined campaign creation tools, Reddit also announced two features to improve the quality of audience and user behavior signals:
1-click Google Tag Manager integration for Reddit Pixel.
Event Manager Quality Assurance (QA).
The platform now offers a 1-click integration with Google Tag Manager (GTM) for the Reddit Pixel, dramatically reducing the friction of installing and configuring conversion tags.
Advertisers can now fire up GTM, install the Reddit Pixel in minutes, and start sending conversion data without needing to pull in a developer. This update alone will make performance-focused advertisers breathe a little easier.
Reddit also upgraded its Event Manager QA tools. The revamped Events Overview now gives a clearer breakdown of conversion events coming from both the Reddit Pixel and the Conversions API (CAPI).
Advertisers can spot data discrepancies faster and ensure their lower-funnel campaigns are set up for success.
Jim Squires, EVP of Business Marketing and Growth at Reddit, noted that SMBs have always been an essential part of the platform’s community and advertising base.
We continue to make improvements to the Reddit Ads Manager that make it easier to launch and manage campaigns, so they can focus on what matters most: growing and running their businesses.
Reddit Ads Continues To Push Forward
With these latest updates, Reddit continues refining its ad platform for a broader range of advertisers, with particular attention to reducing friction for growing businesses.
Advertisers who have been looking for more streamlined ways to import, optimize, and measure campaigns will likely find these tools helpful as they plan their next steps on Reddit.
Have you already tried out Reddit Ads? Will these updates make you lean towards testing a new platform next quarter?
On Friday, Google sent out a subtle but impactful policy update to advertisers, confirming changes to its long-standing “Unfair Advantage Policy”.
While the official enforcement date is April 14, 2025, the conversation has already started — and it’s anything but quiet.
The PPC community is buzzing with opinions, questions, and concerns. But this update didn’t come out of nowhere.
About a month ago, Google quietly laid the groundwork for this change without most people noticing.
Let’s unpack exactly what’s happening, why it matters, and how advertisers are reacting.
What Did Google Change?
The core of the update is about limiting how many ads a business, app, or site can show in a single ad location. Here’s Google’s new language:
The new language is crucial to understand.
The focus isn’t on restricting brands from showing multiple ads across different placements—it’s about stopping advertisers from stacking multiple ads in the same slot, which would effectively block competition and inflate dominance.
It’s not a total ban on multiple ads from the same advertiser showing on a single page, but rather a limit within a specific ad location.
However, as with many Google Ads policies, the phrase “single ad location” is doing a lot of heavy lifting—and advertisers are left wondering how Google will interpret and enforce it in practice.
One notable detail: Google says violations won’t lead to instant account suspensions. Advertisers will receive a warning and at least seven days to address any violations before facing suspension.
This is important. Google seems to be trying to strike a balance between tightening policy and giving advertisers room to adapt.
The Breadcrumb Many Missed – February Auction Documentation Update
Interestingly, this isn’t the first time Google has hinted at this shift.
Back in February 2025, advertisers noticed that Google updated its documentation on “How the Google Ads Auction Works”.
The update clarified that Google runs separate auctions for each ad location, meaning that the auction for the first position is distinct from the auction for the second, third, and so on.
Ginny Marvin, Google Ads Liaison, even acknowledged the change directly in LinkedIn discussions. This detail flew under the radar for many but now seems like a foundational piece for this official Unfair Advantage update.
Effectively, Google was setting the table a month ago. This policy update simply formalizes how those auctions will now prevent advertisers from “double-serving” or stacking ads in the same position.
Why Google Is Doing This, And Why Now
Google’s goal here appears twofold:
Auction Fairness — Google wants to prevent scenarios where advertisers, affiliates, or large multi-account setups game the system by occupying multiple positions within a single auction.
Affiliate Abuse Control — This rule directly calls out affiliates who break affiliate program rules, a growing concern in Google’s search ecosystem.
Of course, some advertisers suspect there’s a third goal: protecting the user experience and, more directly, protecting Google’s own long-term revenue by encouraging more advertisers to compete rather than allowing the largest players to squeeze others out.
Advertisers Give Mixed Reactions to Google Update
While this update was emailed to advertisers on Friday afternoon, marketers didn’t waste time sharing their takes on the update.
Andrea Atzori, who also received the email from Google, took to LinkedIn to provide his take on the update.
Atzori highlighted that this change is more about clarification than transformation, as he’d seen the same advertiser in multiple locations previously.
Navah Hopkins also took to LinkedIn with a more brief update, eager to hear thoughts from fellow marketers on the Unfair Advantage policy.
Hopkins and others noted that while the update may sound fair in theory, the proof will come in how it affects impression share, Auction Insights, and real-world campaign performance.
From the comments on Hopkin’s post, early reactions seem to lead towards skepticism and questions:
Chris Chambers commented:
This is going to be wild from a metric reporting standpoint since it seems like right now it counts as 2 impressions and also affects your impression share and position in Auction Insights (same with competitors). But it also seems like now the advertisers with the most to spend in each niche will get even more real estate and be able to show twice, potentially cutting out smaller competitors completely from the first page.
Steve Gerencser had a similar take to Chambers:
I wonder how they are going to count people that pogo from one ad right back to the next and then back to something else? I can see a lot of wasted ad spend, or an opportunity for someone with deep pockets to dominate.
Some worry that well-funded advertisers will still find ways to dominate, while smaller brands hope this levels the playing field.
What Advertisers Should Watch For
While the policy may not seem earth-shattering at first glance, it does come with a few things advertisers should actively monitor.
First, smaller and mid-sized advertisers may stand to benefit, at least in theory. By limiting how many ads a single business can show in one location, Google could slightly reduce the dominance of big-budget brands that have historically owned the top of the page through multiple placements.
This could open up space for other players to get visibility where previously they were pushed out.
But, as several PPC pros pointed out on LinkedIn, the big question is how Google defines and enforces a single ad location in practice.
Google clarified last month that each ad location runs its own auction, meaning it’s technically possible for a brand to show up in multiple places on the same page—just not in the exact same slot.
So, while the policy aims to limit dominance, it doesn’t necessarily mean fewer total appearances for advertisers with deep pockets.
This also has potential ripple effects on Auction Insights reports. If Google starts filtering or limiting how often multiple ads from the same business appear in a given location, expect impression share metrics and overlap rates to behave differently—maybe even unexpectedly.
Advertisers will need to watch Auction Insights and Impression Share trends closely post-April to see if any patterns emerge.
Additionally, affiliate marketers and businesses using aggressive multi-account or multi-site strategies should be especially careful. The updated policy makes it clear that affiliates must play by their program’s rules and can no longer try to sneak multiple ads for the same offer into the same auction.
While Google says you’ll get a warning before any suspension, it’s probably wise to get ahead of this now, rather than risk a compliance issue later.
And finally, there’s still some ambiguity about multi-brand or franchise setups. If you’re managing a brand with multiple sub-brands, sister companies, or franchisees, the question remains: will Google treat you as one business under this policy or multiple?
That detail could make a big difference, especially for large organizations or verticals like automotive, real estate, or hospitality.
Final Thoughts: Is This Really a Game-Changer?
Honestly? It’s hard to call this a monumental shift yet. The update feels more like a formalization of existing enforcement patterns than a radical new rulebook.
That said, the PPC community is right to question what this will look like in Auction Insights and daily performance reports. Whether this is a minor tweak or the start of stricter anti-duplication policing will become clearer as advertisers see real-world data throughout Q2 and beyond.
Either way, it’s worth watching—especially if you’ve ever benefitted from, or competed against, someone taking up too much SERP real estate.
Ad scheduling is a fundamental setting in Google Ads and Microsoft Ads, but when managing campaigns across multiple time zones, it becomes more complex.
Standard scheduling tactics may not cut it if you’re advertising internationally or running campaigns across regions with different peak engagement times.
Poorly timed ads can lead to wasted budget, lower conversion rates, and missed opportunities.
This article goes beyond the basics to cover next-level strategies for scheduling ads effectively across different time zones.
We’ll explore techniques such as localized scheduling, data-driven adjustments, and automation to maximize campaign performance.
Understanding Time Zone Challenges In PPC
When advertising across multiple regions, time zone discrepancies can create challenges that impact ad delivery, engagement, and conversions.
A common pitfall is assuming that a single campaign schedule will work universally. In reality, what works in one location might be completely ineffective in another.
For example, if your Google Ads account is set to Eastern Time but your target audience is primarily on the West Coast, your ads might be running during off-hours, leading to suboptimal performance.
International campaigns require even more diligence to consider local business hours and consumer behavior patterns.
Another factor is peak engagement hours. While lunchtime or evening hours may be prime time in one country, those same hours could be completely irrelevant in another.
Understanding these nuances is essential for optimizing your ad scheduling strategy.
Advanced Strategies For Scheduling Ads Across Time Zones
Successfully managing ad scheduling across time zones requires a thoughtful approach that goes beyond the basics.
While many advertisers set simple schedules and hope for the best, the real wins come from leveraging automation, data-driven insights, and strategic segmentation.
Whether you’re running campaigns domestically across U.S. time zones or managing international PPC efforts, applying advanced techniques can help ensure your ads are served at the right time for the right audience.
Segmenting Campaigns By Time Zone For Better Control
If you’re running campaigns across multiple time zones, one of the best ways to stay in control is by creating separate campaigns for different regions.
This lets you adjust ad schedules, budgets, and bidding strategies based on local peak performance times rather than forcing a single schedule to work for every location.
For example, an ecommerce brand serving customers in the U.S. and Europe might run separate campaigns for each region.
The U.S. campaign can focus on morning and evening hours when engagement peaks, while the European campaign targets prime shopping hours in local time zones.
While this approach adds complexity, the benefits far outweigh the extra management effort. Automating adjustments with rules and scripts can help streamline this process, ensuring each campaign is optimized without constant manual oversight.
Leveraging Automated Bidding Over Fixed Schedules
Manual ad scheduling has its place, but automated bid strategies like Target ROAS or Maximize Conversions allow you to optimize bids dynamically rather than setting fixed hours.
These AI-driven approaches adjust bids in real time, ensuring ads appear when conversion probability is highest, regardless of time zone differences.
For instance, if data shows that users in one region convert at a higher rate between 9 a.m. and 11 a.m. but another region performs better in the evening, automated bidding will allocate more budget when it matters most.
Instead of manually adjusting bids every few weeks, let machine learning do the heavy lifting.
Optimizing Scheduling Based On Market-Specific Peak Hours
Different markets have different user behaviors, so it’s crucial to base your scheduling decisions on actual performance data rather than assumptions.
Google Ads’ ad schedule reports and Microsoft Ads’ time-of-day insights can help you identify when users in each region are most active.
For example, if analytics reveal that North American users are most engaged in the evening while European users peak in the morning, your campaigns should reflect that.
Instead of blanketing all markets with a generic ad schedule, tailor your approach based on real-time engagement trends.
Using Labels To Manage And Adjust Scheduling
One often overlooked yet powerful feature in Google and Microsoft Ads is the use of labels.
Labels let you group campaigns, ad groups, or keywords into easily manageable categories, making it simpler to track and adjust schedules.
For example:
Tagging campaigns by region allows for easy bulk adjustments when shifting schedules due to seasonal changes or promotional events.
Labeling time-sensitive ads ensures that you can quickly pause or resume campaigns as needed without sifting through dozens of settings.
Using automation scripts with labels enables automatic bid adjustments or scheduling changes based on real-time performance.
By applying labels effectively, you can streamline scheduling changes without manually editing each campaign, saving time and reducing errors.
Automating Scheduling Adjustments With Scripts
If you’re managing multiple time zones, Google Ads scripts can be a game-changer.
Rather than manually adjusting schedules, scripts can dynamically modify bids based on real-time performance data.
For example, a script could be set up to boost bids by 20% during high-converting hours and reduce them by 10% when conversions drop. This keeps campaigns optimized while freeing up time to focus on strategy rather than daily bid adjustments.
Scripts also work well with labels. You can program scripts to modify bid strategies for campaigns tagged with specific labels, ensuring changes are applied only to relevant ads.
Adjusting For Daylight Saving Time Changes
Another scheduling headache is Daylight Saving Time (DST), which varies by country and can cause misalignment in ad schedules.
A campaign that ran perfectly last month might suddenly be off by an hour if a region switches to DST.
To avoid this, maintain a calendar of DST changes in key markets and adjust schedules proactively.
Another option is using automated rules or machine learning-based bid adjustments to handle these shifts without manual intervention.
Budget Allocation Based On Regional Performance Trends
Rather than splitting your budget evenly across all time zones, consider allocating more spend to the highest-performing regions based on historical data.
By analyzing performance reports, you can determine which locations deliver the best ROI and adjust budgets accordingly.
For instance, if your data shows that conversions peak in the late evening for Pacific time zone users but decline in the early morning for Eastern time users, shift more budget toward the stronger-performing time periods.
This approach ensures ad spend is being used effectively rather than wasted on time slots that don’t generate conversions.
Mastering Ad Scheduling For Global Success
Effectively navigating time zone differences in Google and Microsoft Ads isn’t just about setting a schedule and forgetting about it.
A winning strategy requires a mix of localized segmentation, automation, and continuous data-driven adjustments.
Instead of seeing time zone variations as a challenge, think of them as an opportunity to refine and optimize your strategy.
By leveraging campaign segmentation, smart bidding, labels, and scripts, you’ll gain greater control over when and where your ads appear – without unnecessary budget waste.
At the end of the day, great PPC management isn’t about simply keeping the lights on. It’s about making smart, strategic moves that maximize impact.
Test, tweak, and refine your approach, and you’ll see the results in both efficiency and performance.
When it comes to luxury fashion, the likes of Louis Vuitton, Chanel, and Gucci are among the most well-known, and rightfully so. These three alone are valued at over $87 billion.
Over the last decade and a half, I have had the opportunity to work extensively within this market.
Managing PPC in this space is super interesting as it requires a shift in thinking away from cookie-cutter ecommerce PPC strategies.
It’s not just a case of the product value being above average, leading to longer, more considered decision-making processes.
Luxury audiences are unique. They differ by brand, and finding success with PPC requires careful consideration and a holistic understanding of digital performance – not just the data within the accounts.
Understanding The Luxury Fashion Consumer
Luxury consumers have unique characteristics and behaviors that mold their purchase motivations and preferences.
You may be thinking through a lens of tactical management and day-to-day operations, “But how does this really impact PPC?”
But it really does.
Broadly speaking, luxury fashion consumers can be divided into three groups:
Ultra-High-Net-Worth Individuals (UHNWI): Buyers with plenty of disposable income who frequently purchase luxury fashion without too much consideration for price.
Aspirational Consumers: Middle- to high-income buyers who occasionally purchase within their budget to match their lifestyle.
New Luxury Shoppers: Younger buyers, particularly Millennials and Gen Z, who engage with luxury brands online and purchase more infrequently, likely to have more of an affiliation with pre-loved luxury.
From a motivational perspective, elements such as social standing, quality, heritage, and storytelling play an important role. They can feed into ad copy and/or landing page messaging to test the impact on PPC.
Let’s say a brand is only bidding on new customers through Google Ads with a basic strategy of “brand keywords” (e.g. “louis vuitton”) and “generic keywords” (e.g. “luxury handbags”).
Focussing on generic keywords, these three audience groupings have to be considered – and segmentation is absolutely essential – as they all search using the same queries and are served the same SERP:
Screenshot from search for [buy luxury handbags], Google, February 2025
It differs on a case-by-case basis, but the customer lifetime value (CLV) of a “New Luxury Shopper” will likely be vastly different from users in the “UHNWI” grouping.
Aspirational buyers now make up 18% of the luxury fashion market across key economies. By 2030, Gen Z is projected to contribute 25-30% of luxury market purchases, with Millennials making up the majority at 50-55%.
There are plenty of opportunities for brands to benefit from when scaling up their PPC spend.
Segment performance data, get a clear view of which grouping demands the highest portion of media spend, and ask yourself whether this can be improved and how this data can be used to drive better results.
The Complexity Of Luxury Purchase Paths
Deliberated decision-making is a key element to consider when measuring PPC performance in luxury fashion.
A study found that two-thirds of Chinese luxury goods consumers undertake thorough product research before making luxury purchases, with over 40% of the respondents sharing that they used photos and pricing comparisons and compared to similar products before making the purchase.
Reporting week-to-week on ROAS/CAC may not be enough, and if decisions are made using windows that are too short/long, optimizations won’t align with the overarching goal.
I’ve seen growth stunted because of this, and access to purchase path data is a must.
The journey to purchasing luxury as a whole (not even considering the three audience groupings) involves many interactions.
A consumer insights study from NP Digital found that as the price point grew, the number of touchpoints for B2C increased.
For example, from $100 or less to $10,000+, the number of touch points moved from 8 to 23, and while price point doesn’t necessarily mean luxury, it shows a strong correlation.
This, combined with 80% of all luxury sales being digitally influenced, shows the importance of having connected data that can be fed into PPC strategies.
Take a scenario where a brand’s new customer acquisition through PPC is bulletproof, but social content is thin, and lead nurturing is non-existent.
This fractures the decision-making process, which ultimately impacts the results driven through PPC as demand will stay consistent and budget will be spent.
Still, the needs of the consumer are not met, which would mean fewer sales, a lower conversion rate, and declining efficiency.
A long-term view is essential for both reporting and optimization, and the necessity for brands to adopt an integrated marketing strategy has never been more critical.
The Influence Of Trends And Tradition
A survey found that 70% of consumers identify as trend-focused, and 77% value tradition.
Trend-focused consumers are driven by the latest movements in luxury fashion, whether this is the latest collab, a change in leadership, or a trending style – all of this will influence PPC.
These trends can occur steadily over time or, in some cases, emerge overnight due to the media, social influence, etc.
Depending on how an account is set up, the flow of search queries over time for “brand” keywords, “brand + product,” “product,” and “generics” will experience ebbs and flows based on a wealth of factors.
Knowing why certain products become more popular than others and being able to pull this data out of the accounts to share with the wider team is essential.
This should be reported as frequently as platform ROAS or blended CAC, not just on an ad hoc basis.
This can be done a few ways, one being to group search queries together, categorize them, and report using an index vs. the average, for example:
Automate the export of all search queries on a daily basis (or longer, depending on the size of the brand and budget) with a date stamp.
Create a script (or use Python/equivalent) – or find a partner to build one for you – to categorize these queries into groupings, or do this manually to get started.
Build a dashboard that maps the groupings over time and benchmarks vs. the average to provide a clear view of increases/decreases in demand, sales, cost, etc.
Map an index to each category and rank these based on the average.
It may take some work, but it is definitely worth it, considering how often trends and seasons change in luxury fashion.
With this data, brands can look at trends over time and react much quicker than simply seeing a better or worse ROAS one week vs. the previous week.
From a holistic perspective, this data can be shared across channels (e.g., SEO, PR, and content) and with the wider team, such as buyers, to share insights ahead of time on what users are searching for and why.
Holistic Performance Measurement
A robust measurement strategy is paramount when managing PPC in any capacity, and even more so for luxury fashion.
Gone are the days when teams were asked, “How much did we put in and how much did we get back?” at an ad platform level (well, almost gone).
Luxury brands are leaning into smart attribution modeling, focusing on CLV and personalization, to find the true value of their PPC activity.
This goes much further than an out-of-the-box attribution solution.
Unified Data Collection
Luxury brands spend millions annually on advertising, with 33% of this cost allocated to digital.
Measuring PPC performance through one or two lenses isn’t enough as it influences (and is influenced) by all other channels.
Stores, pop-ups, and events are incredibly important for luxury fashion brands, and being able to target new and returning users at key touchpoints through PPC is a must.
Pulling together online and offline data across multiple markets, channels, and sources requires robust processes, ownership, and consistency.
And, as easy as it is for me to write this, it most likely isn’t a quick task and will take considerable time and resources, but the payoff is certainly worth it to have a clear view of PPC performance.
Attribution Modeling
In this day and age, models such as “last click” are a touchy subject, and with good reason.
Consider all the touchpoints, advertising channels, and budget invested in delivering great customer journeys that luxury buyers experience for billion-dollar brands to then turn around and say the last click before the sale gets 100% of the credit.
In-platform, brands use multi-touch attribution (such as Google data-driven attribution or DDA), which is the best of the bunch, but still very limiting.
To begin to bridge the gap between online and offline, Louis Vuitton and Gucci invested heavily in omnichannel data platforms that allow them to connect in-store activity with digital interactions.
Another way brands are moving towards a more connected view of performance is through Marketing Mix Modelling (MMM), a methodology that was first used in the 1950s.
This takes attribution a step further by measuring the impact of PPC (for example) on the broader marketing ecosystem, quantifying PPC’s role within the entire marketing mix.
The complex decision-making process for luxury fashion makes having this connected data a non-negotiable for effective PPC budgeting, optimization, and growth.
Customer Lifetime Value
This metric refers to the total profit a customer is expected to drive over the duration of their relationship with a brand.
New customer acquisition through PPC is often measured through in-platform/blended ROAS and CACs, both serving a purpose when analyzing platform performance.
However, luxury brands that adopt CLV are able to identify which customer segments are the most profitable and take a longer-term view of acquiring high-value customers while tailoring strategies to audiences that aren’t as profitable to them.
Let’s look at an example of Brand A, who sells luxury handbags:
Current KPI for paid search: Account-wide Google Ads in-platform ROAS >5.
Brand A runs a data analysis project to find CLV by user segment and discovers that Audience 1 spends $11,000 over their lifecycle with the brand, and Audience 2 spends $4,000.
Their new KPIs would look vastly different, which feeds into budgeting, setting bidding strategy targets, promotions, and more.
Looking at the longer term, having this data gives luxury brands the benchmarks to work from to improve CLV numbers, which will, in turn, allow more freedom for scaling PPC spend.
Bringing It All Together
PPC doesn’t exist in isolation for any brand.
It’s influenced by many factors, and looking at one metric or platform for the answers isn’t enough.
Luxury fashion operates differently from a standard ecommerce retailer with unique audience profiles, extended decision-making processes, and an ever-moving flow of trends and traditions.
I’ve seen luxury brands dive headfirst into advanced data modeling, then go back to the drawing board to set accurate KPIs that fuel the fire of scaling media spend on PPC.
At the same time, plenty of luxury fashion brands use arbitrary in-platform modeling (e.g., last click) as the source of truth.
Aside from the obvious (cost, time, resource, etc.), there are only benefits from digging into the data to improve PPC performance from one end (e.g., setting bids, messaging, etc.) to the other (e.g., budgeting, market/network expansion).
As Google’s Ad Product Liaison, I often share updates and insights with the community of digital advertisers and, best of all, get to hear your feedback first-hand.
We heard quite a lot after our recent announcement that, after a period of beta testing, we’re rolling out negative keywords in Performance Max (PMax) campaigns with a restriction.
We had set a cap of 100 negative keywords per campaign.
While the ability to add negative keywords in PMax directly in Google Ads without having to request them through Support or an account rep has been a long-time ask, we heard very quickly that the cap of 100 negative keywords felt too restrictive for many.
Here’s a look behind the scenes at the reasoning behind the initial cap, what we learned from your feedback, and the subsequent decision to increase the limit to 10,000 negative keywords per campaign.
Why The Cap In The First Place?
AI, by its nature, thrives on flexibility, adapting to real-time data and user behavior.
Performance Max is an AI-powered, goal-based campaign type that’s designed to find conversions based on the goals you set.
The intention of capping negative keywords in PMax at 100 was to give advertisers additional control while still giving PMax the flexibility to achieve your campaign’s stated goal – a limit of 100 negatives felt like a reasonable starting point.
To arrive at that number, we analyzed PMax campaigns in which negative keywords had been added via Support or their account rep.
We found that the 100-keyword limit would cover the vast majority of campaigns using negative keywords.
We also saw that the majority of submitted negative keywords had no actual serving impact – their ads already weren’t triggering for terms advertisers had concerns about.
In many other cases, other targeting exclusions would have been more suitable for blocking unwanted traffic.
We saw this in our beta testing as well. In short, 100 felt like a good compromise between offering enough flexibility without dramatically increasing the risk of accidentally blocking valuable traffic.
The initial cap of 100 negative keywords aimed to:
Preserve AI Optimization: Excessive negative keywords can act as rigid constraints, preventing the AI from exploring valuable search paths and hindering its ability to identify emerging trends. Essentially, it can stifle the algorithm’s ability to find the most efficient conversions. Very large negative keyword lists can potentially negatively impact the machine learning systems and hurt performance.
Prevent Accidental Traffic Exclusion: We aimed to prevent advertisers from inadvertently excluding valuable traffic through overly broad negative keyword scopes and missing potential high-intent customers.
What Your Feedback Told Us
We heard advertiser feedback loud and clear that while negative keywords are welcomed, the cap of 100 felt too restrictive.
We heard from brands that quickly hit the 100 limit before including the key themes they wanted to negate. In short, it wasn’t a practical solution for many.
After looking at options, the team agreed to align with the limits in Search campaigns and raise the threshold to 10,000 negative keywords per PMax campaign.
That’s obviously a significant jump from 100 and way more than nearly every business will need or should use, but aligning on one common threshold simplifies things and gives advertisers plenty of room to experiment.
Actionable Insights And Considerations For Measuring Impact
Adding negative keywords to a Performance Max campaign can, of course, impact where your ads show on Search and Shopping inventory.
While the increased limit provides greater control, it’s crucial to use negative keywords strategically. Here are several things to keep in mind when applying negative keywords in PMax:
Judicious Application: Avoid overly broad exclusions that might hinder the AI’s ability to find valuable conversions. Prioritize high-impact negatives that address specific ROI concerns. Keep in mind that account-level negative keywords you’ve added for brand suitability purposes already apply to your PMax campaigns.
Match Type Precision: Understand the nuances of broad, phrase, and exact match negative keywords in PMax. Negative match types work differently than their positive counterparts. For negative broad match keywords, your ad won’t show if the search contains all your negative keyword terms, even if the terms are in a different order. Phrase match negatives exclude queries containing the exact phrase, while exact match excludes only the specific query. Use them strategically to balance precision and reach.
Performance Monitoring: Closely monitor key metrics like conversions, conversion value, and conversion rates to ensure negative keywords have a positive rather than negative impact on performance.
Conflict Resolution: Be aware that if a user search matches both a positive signal and a negative keyword, the negative keyword will take precedence, and your ad will not be eligible to serve for that query.
Beyond Negative Keywords: Remember that PMax offers other control mechanisms to inform when your ads can trigger on Search.
Regular Audits: Just as with your Search campaigns, be sure to regularly audit your negative keywords to identify where you might be blocking potential valuable traffic. And Search Term Insights can help you identify query themes and individual search terms you might want to block with negative keywords.
Your Questions Answered
I received several questions about this update from advertisers on LinkedIn and X (Twitter) and want to address some of those here.
“The real challenge is how negative keywords interact with PMax’s black-box decision-making. Will we get more visibility into which search terms PMax is actually serving against? And how will negatives impact machine learning optimization long term?”
While PMax is designed to automate many aspects of campaign management, we recognize the importance of providing advertisers with meaningful insights.
The introduction of negative keywords is one of several recent steps towards providing additional controls.
Search Terms Insights for PMax provides a view of the search term categories as well as specific search terms that triggered your ads in Search. You’ll find performance metrics at the search term level.
Search Terms Insights is designed to make analyzing search term data easier by already grouping similar searches into broader categories, saving you the time to sift through individual search terms.
This data can be downloaded and available via scripts and the Google Ads API.
As for the long-term impact of negative keywords on campaign optimization, it’s important to strike a balance.
While negative keywords provide crucial control, an overly restrictive approach could limit the system’s ability to learn and adapt to new opportunities.
As noted above, our recommendation remains to use negative keywords strategically to exclude truly irrelevant traffic, allowing the AI to continue exploring and finding valuable conversions within the defined boundaries you set.
Reporting and insights are areas the team is actively focused on. Stay tuned for more on this.
“Google never needed <100 negative keywords in order to have>
Our intention was never to encourage spending on irrelevant queries.
Performance Max is a goal-based campaign type which means it’s designed to find more of the conversions that you indicate are valuable to your business.
The initial cap of 100 negative keywords was tested in beta and seemed to provide a reasonable level of control while still allowing the AI the necessary flexibility.
We acknowledge that our initial assessment was not sufficient for many advertisers, and that’s why we listened to your feedback and made the significant increase to 10,000.
“Why can’t negative keywords be limitless at any/every account level? Are there technical/operational issues that would be impacted?”
This is a fair question. There are limits on certain entities in Google Ads accounts to help ensure system and process stability. We have more details on various entity limits here.
“Will Google give us the ability to see the previously applied negative keyword lists we used to do via Support or our reps.”
Yes, you’ll be able to see and edit negative keywords and negative keyword lists that were previously added by Support or a rep.
“Why weren’t negative keywords available from the very start when PMax launched.”
The core principle behind PMax is leveraging AI to discover conversions across Google’s channels.
When PMax launched in 2021, the vision was to give advertisers a streamlined way to tell Google what they want to optimize for and then allow the system to learn and find those desired customers across all of Google’s inventory.
Exclusions were seen as unnecessary and potential impediments to optimization.
Over time, and with advertiser feedback in mind, features within PMax have expanded. And the pace of new insights and controls has been accelerating in recent months.
“What about negative keyword lists?”
Many of you asked about the possibility of using negative keyword lists within Performance Max campaigns, as you can in Search campaigns.
We are actively working on this and expect to have more to share on support for negative keyword lists in PMax later this year.
How PMax Is Evolving
I recently shared the overview below of many of the recent reporting and control updates for PMax at the Paid Search Association Conference.
These features are aimed at giving you more tools and information to steer PMax to find more of the conversions you want to generate for your business.
Features like brand guidelines help ensure your responsive display ads and auto-generated video ads reflect your brand’s visual identity.
Recent controls and insights updates for PMax. Image from author, March 2025
Stay tuned for more on search terms data and analysis capabilities as well as additional insights this year.
This is an area we are actively focused on. And keep the feedback coming.
Many high-intent customer interactions, especially in industries like healthcare, legal, home services, and B2B, happen over the phone.
If you’re in an industry that receives any number of calls, you may be struggling to connect these calls to your digital marketing efforts, leading to:
And despite the high percentage of customer interactions happening over the phone, many brands fail to track which ad or campaign led to those calls.
This could stem from knowledge gaps, tight budgets, or reluctance to integrate more technology into their stack.
Without call attribution, businesses are left guessing about what’s driving revenue.
What Is Offline Conversion Attribution?
Offline conversion attribution is the process of linking your online marketing efforts to offline sales or actions.
It helps you understand which digital marketing channels and campaigns contribute to offline conversions, such as in-store purchases, phone call inquiries, or signed contracts.
How Offline Conversion & Phone Call Attribution Works
By paying attention to phone call conversion data, you can:
1. Connect Online Interactions To A Phone Call: A user clicks on a digital ad, visits a website, fills out a form, or calls a business after seeing an online campaign. 2. Store User Data In One Place: Data from these interactions (such as email, phone number, or a unique tracking ID) is captured and stored. 3. Match Callers With Offline Events: When a purchase or conversion happens in-store, over the phone, or through a sales team, businesses match it back to the initial online touchpoint. 4. Analyze & Optimize Webpages With Content That Converts: You can analyze which digital campaigns, keywords, or ads drive the most offline conversions, optimizing their marketing strategy accordingly.
What You Can Do With Phone Call Conversion Data
When you introduce a tool that acts as Google Analytics for phones, you’ll be able to:
Improve ROI Measurement: Helps businesses understand the real impact of digital marketing on offline sales. Enhance Ad Targeting: Enables better retargeting of high-intent users. Optimize Budget Allocation: Allows marketers to invest more in channels that drive actual sales, not just clicks or website visits. Bridge the Online-Offline Gap: Particularly important for industries like retail, automotive, healthcare, and B2B, where many transactions happen offline.
Examples of Offline Conversion Attribution
A customer finds your business through organic search.
They see a retargeting ad on Facebook.
Finally, they click a PPC ad and call to book an appointment.
Without call tracking, the PPC ad might receive full credit, even though SEO and social played key roles. Choosing the right attribution model ensures data-driven marketing decisions.
Best Tools for Offline Conversion Tracking
Google Ads Offline Conversion Tracking
Facebook Offline Conversions API
CRMs like HubSpot or Salesforce
Call tracking software like CallTrackingMetrics
SEO & Call Tracking: Connecting Organic Efforts To Real-World Conversions
Gain Keyword Attribution Beyond Clicks
Rankings, traffic, and forms typically measure SEO success fills. But what about phone calls? Call tracking technology with dynamic number insertion (DNI) allows businesses to:
Identify which organic search queries lead to phone calls
Optimize content around real customers’ questions and concerns
Understand which landing pages drive the most offline conversions
For example, if multiple callers reference a specific product-related question, that insight can inform new blog topics or FAQ pages to improve SEO efforts, driving even more right-fit traffic into your sales funnel and conversion metrics.
Optimize For True Local SEO
Local search is a major driver of inbound calls. When combined with call tracking, businesses can finally understand:
Which local listings (Google Business Profile, Yelp, etc.) generate the most calls?
What information do customers search for before calling?
How to refine location-based content for higher engagement
How Call Insights Can Strengthen Your SEO Strategy
Phone calls aren’t just conversions—they’re valuable sources of customer insights that your teams can use to refine ad strategies, train teams on sales pitches, and identify areas for growth in your content strategy. Each conversation has the potential to reveal the common questions, pain points, and content gaps that businesses can address to improve their marketing performance.
1. Identify FAQs for Stronger Content
Often, customers call a company’s support phone number when they can’t find information online, either about a product or service they’re considering buying or one they’ve already purchased. By analyzing call transcripts, businesses can spot recurring questions and proactively address them in blog posts, FAQs, or product pages.
For example, if a home services company frequently gets calls asking, “Do you offer emergency repairs on weekends?”, this signals a need to make that information more visible on their website. A dedicated service page or blog post could reduce unnecessary calls while improving customer experience.
2. Refine Your Website Messaging
If callers repeatedly ask about pricing, product differences, or service details, your website messaging probably isn’t clear enough.
For instance, an e-commerce brand selling fitness equipment might notice that callers often ask, “What’s the difference between your basic and premium treadmill?” Adding a simple comparison chart or explainer video can help lessen confusion and improve conversions.
3. Fill Content Gaps To Reduce Sales Friction
Repeated calls about the same topic are a good indicator of missing or unclear content. A B2B SaaS company, for example, might receive frequent inquiries about integrating with a particular CRM or social platform. Instead of solely relying on customer support, the marketing team could identify this pain point and create a step-by-step guide or video tutorial to address it, which would reduce friction and improve self-service for prospects.
PPC & Call Attribution: Maximizing ROI With Better Insights
Tracking clicks alone doesn’t reveal the full ROI of PPC campaigns. Many conversions, especially phone calls, happen offline and go untracked. Without attribution, businesses may waste ad spend and overlook high-intent leads. This section explores how call tracking connects PPC efforts to real conversions, improving marketing efficiency.
Paid Search: Wasted Spend Without the Full Picture
A high cost-per-click (CPC) doesn’t guarantee strong ROI if businesses aren’t tracking offline conversions. Without call tracking, marketers risk:
Over-investing in underperforming keywords
Missing opportunities to optimize campaigns for call-driven leads
Failing to attribute revenue-generating phone calls to PPC efforts
When a business fails to account for ROI in the form of phone calls, they’re losing an opportunity to accurately account for their real CPC and allocate resources accordingly.
Call Tracking + Google Ads = Smarter Bidding
PPC campaigns are only as effective as the data behind them. Without tracking phone calls, businesses risk misallocating budgets to keywords that drive clicks but not conversions. Integrating call tracking with Google Ads provides a clearer picture by linking calls to the specific campaigns, ad groups, and keywords that drive valuable conversions.
With AI-powered call scoring, marketers can identify high-intent leads and adjust bidding strategies based on actual conversion data—not just clicks. This ensures ad spend is focused on quality leads rather than wasted traffic.
Retargeting with First-Party Data
Not every caller converts immediately. Call tracking allows businesses to retarget high-intent leads with personalized follow-ups. By analyzing call topics, marketers can tailor ads or email sequences to address specific customer concerns, increasing the likelihood of conversion.
Additionally, integrating call data with CRM platforms like HubSpot and Salesforce ensures sales teams can nurture prospects effectively, preventing lost opportunities. By combining PPC insights with offline conversions, businesses gain a clearer understanding of customer behavior, leading to smarter ad spend and more targeted outreach.
Back To Basics: Omnichannel Attribution & The Power Of Call Data
As marketing shifts to a mix of online and offline tactics, attribution models must evolve. By integrating call tracking with Google Analytics, CRM systems, and automation tools, businesses can gain a complete view of the customer journey.
A company that integrates CallTrackingMetrics with Google Analytics and its CRM can:
See exactly which campaigns drive calls.
Automate follow-ups based on conversation insights.
Optimize for higher-value interactions.
AI & Conversation Intelligence
Call tracking is no longer just about recordings or basic attribution. AI-driven call analysis provides deep insights, such as:
Customer intent and sentiment analysis.
Common objections that impact sales.
Automated lead qualification based on real conversations.
By leveraging AI, businesses can better understand customer needs, improve sales strategies, and ensure marketing efforts are driving meaningful engagement. Implementing AI-driven call tracking empowers teams to make data-backed decisions that enhance both customer experience and conversion rates.
Proving Marketing’s True Impact
Marketers are often challenged to prove ROI beyond what we might call “vanity metrics”, like impressions and clicks. Though these have a place in any strategy, these metrics don’t necessarily move the needle toward sales goals.
Call tracking, on the other hand, delivers revenue-focused attribution, showing exactly how digital marketing contributes to bottom-line growth. This kind of revenue-focused attribution can help an entire company analyze past efforts and accurately forecast revenue based on real campaigns, real calls, and real results
Case Study: This study from CallTrackingMetrics demonstrated how AI-driven call tracking optimized PPC ROAS and improved lead quality.
PPC reporting has always been both essential and frustrating. It’s essential to keep clients engaged by informing them of the results you’re driving.
But it’s also frustrating because of data discrepancies, cumbersome analysis, and the time required to share understandable, jargon-free reports with different stakeholders.
Fortunately, AI is turning these obstacles into opportunities by filling in gaps left by privacy-compliant tracking, surfacing insights hidden in overwhelming data sets, and automating reporting so it meets the needs of every stakeholder.
In this article, I’ll walk you through some of the technology used by modern marketers and share examples of how I’ve used AI to streamline my PPC reporting.
1. Collect Complete And High-Quality PPC Data
We need data to guide us before we can optimize accounts and share our wins, so let’s start there.
The Problems With Data Before AI
Inconsistent and missing data plague PPC efforts.
Google, Meta, Microsoft, and Amazon operate in their own silos, each taking credit for all conversions that have any touchpoint with their platforms. This leads to double counting, making it difficult to decide where to allocate budgets for optimal results.
In other words, the data between the various ad platforms is inconsistent. Specifically, the conversion value advertisers see in their business data may be lower than the sum of all conversion values reported by the ad platforms.
Add to this the challenge of missing data. Privacy regulations like GDPR and Apple’s iOS changes limit tracking capabilities, which causes data loss, incomplete conversion paths, and gaps in attribution.
Marketers who rely heavily on pixel-based or third-party cookie tracking, both of which became unreliable due to browser restrictions and user opt-outs, see a continuous decline in the quality of the data they need to operate.
While AI can’t magically give us perfect data, it can fill in gaps and restore insights, so let’s take a look at some of the solutions in this space.
AI-Driven Solutions For Data Hygiene And Compliance
These platforms aggregate data from multiple sources, giving marketers a comprehensive view of the customer journey.
Example:
A retail brand can use AMC to evaluate how its Google and Facebook ads influence Amazon purchases. Based on what they find, they can re-allocate budgets between platforms to maximize overall return on investment (ROI).
Clean rooms themselves aren’t an AI innovation; however, they benefit significantly from several AI capabilities.
For example, Meta’s Advantage+ uses clean room insights to build lookalike audiences while staying privacy-compliant.
2. Modeled Conversions
While clean rooms are great for unifying cross-platform data, their usefulness is predicated on data completeness.
When privacy regulations make it impossible to get all the data, clean rooms like Google Ads Data Hub and Amazon Marketing Cloud use AI-powered modeled conversions to estimate user journeys that can’t be fully tracked.
Modeled data is also used by tools like Smart Bidding, which leverages machine learning to predict conversions for users who opted out of tracking.
For users who opt out of tracking, Consent Mode still allows the collection of anonymized signals, which machine learning models can then use to predict conversion likelihood.
Example:
Google’s Smart Bidding leverages machine learning to optimize bids for conversions or conversion value.
In cases where conversion data is incomplete due to user consent choices or other factors, Smart Bidding can use modeled conversions to fill in gaps and make good bidding decisions.
The models do this by identifying patterns and correlations between user attributes, actions, and conversion outcomes.
While modeled conversions offer significant benefits in their ease of use (they’re basically provided without any extra effort by the ad platforms), it’s important to remember that they are only estimates and may not be perfectly accurate in all cases.
Advertisers should consider using modeled conversions in conjunction with other ways to get a more complete picture of campaign performance.
For example, advertisers can use Media Mix Models (MMM), a Marketing Efficiency Ratio (MER), or incrementality lift tests to validate that the data they are using is directionally correct.
3. Server-Side Tagging And First-Party Data Integration
Server-side tagging lets marketers control data collection on their servers, bypassing cookie restrictions.
Server-side tagging captures anonymous pings even when cookies are declined, feeding better signals into Google’s AI models for more accurate conversion modeling.
This gives AI more complete data when doing things like data-driven attribution (DDA) or automated bidding.
Illustration by author, February 2025
Example:
An ecommerce company transitions to server-side tagging to retain high-quality data even when technologies like Safari’s Intelligent Tracking Prevention (ITP) break JavaScript-based tracking.
As a result, the advertiser sees a complete picture of all the conversions driven by digital marketing and can now justify higher bids, which makes them more competitive in the ad auction and boosts total sales for their brand.
Actionable Tips:
Implement GA4 Consent Mode and server-side tagging to maintain accurate performance data.
Leverage data clean rooms to analyze cross-platform conversions securely.
Use modeled conversions to fill tracking gaps caused by privacy restrictions.
2. Extract Data Insights And Make Smarter Decisions
Now that we’ve covered technologies that can stem the decline in access to data, let’s examine how AI can help make sense of it all.
The Problem With Data Analysis Before AI
Marketers may struggle to extract actionable insights when looking at a mountain of PPC data.
Humans simply aren’t as good as machines at detecting patterns or spotting anomalies in large data sets.
While statistical methods have long been used to find these patterns, many marketing teams lack the expertise to do it themselves or have no access to a qualified analyst to help them.
As a result, teams miss opportunities or spend more time than they can afford looking for signals to guide optimization efforts.
AI Solutions For Data Analysis And Attribution
1. Data-Driven Attribution Models (DDA)
DDA isn’t the newest solution in attribution modeling, but it exists largely because AI has become cheaper and more accessible.
It solves the problem of assigning values to different parts of the consumer journey when users take a multitude of paths from discovery to purchase.
Static attribution models lack the sophistication to account for this and cause advertisers to bid incorrectly.
Unlike static models, DDA dynamically adjusts credit allocation to reflect the many ways consumers behave.
Machine learning, a form of AI, is what enabled Google to make this more advanced attribution model available to all advertisers and what has driven the steady improvement in results from Smart Bidding.
Recently, I tested GPT Operator to streamline several PPC reporting workflows.
Operator is OpenAI’s tool that lets the AI use a web browser to achieve tasks. It goes beyond searching on the web; it allows you to follow links, fill in forms, and interact intelligently with websites.
In one task, I asked Operator to download auction insights, visualize the data using Optmyzr’s Auction Insights Visualizer, and email a report.
It handled the data transfer and visualization steps flawlessly, though it struggled with taking a clean screenshot instead of attempting to attach HTML.
Illustration by author, February 2025
This illustrates how AI agents can help when data lives in disparate places. There are no APIs available to move it, as is the case with auction insights data from Google.
While Operator still needs too much hand-holding to be helpful today, it seems likely that we’re less than a year away from when it can do many tedious tasks for us.
3. Advanced Statistical Analysis Available To Anyone
Before AI advancements, conducting a statistical analysis could be a labor-intensive process requiring specialized software or data science expertise.
But today, generative AI enables marketers to explore these areas that were previously firmly outside their realm of expertise.
For example, GPT can explain and execute a process like a seasonality decomposition. AI can quickly write Python code that breaks down campaign data into trend, seasonal, and residual components, helping marketers uncover patterns they can act on.
Inspired by this, I used GPT’s Advanced Data Analysis feature to upload weekly Google Ads data and run a full decomposition.
GPT efficiently cleaned the data, identified issues like formatting errors, and generated a breakdown of trends, seasonal variations, and residual fluctuations.
In the analysis, GPT flagged recurring trends, allowing me to pinpoint peak demand periods and optimize bid strategies ahead of time. Tasks that previously took hours now take just a few minutes.
On a side note, I have found large language models (LLMs) so helpful with coding that I am now using v0.dev almost weekly to create apps, browser extensions, and scripts on a weekly basis.
3. Communicate Results Effectively Across Teams
With solid data in place and AI-fueled ways to speed up analysis, we should have some great results to share with stakeholders.
But sharing results through reports has traditionally been one of the most time-consuming and least loved tasks that fall on the plate of the typical account manager. And there were other problems, too.
The Problem With Sharing Reports Before AI
Reports were often static, one-size-fits-all documents that failed to meet the needs of different stakeholders.
Executives required high-level summaries focused on ROI, marketing strategists needed cross-channel insights, and PPC specialists required detailed campaign data.
Customizing reports for each audience was time-consuming and prone to error.
AI Solutions For Tailored Reporting
1. LLM Report Summarization
LLMs such as Claude, Gemini, and ChatGPT can quickly generate different explanations of reports from the same underlying data, enabling efficient customization for each audience.
For example, ChatGPT can produce a concise executive summary alongside a more detailed keyword-level report for PPC teams.
But that customization can and should be taken even further. In OpenAI, it’s possible to create custom GPTs, each with its own instructions. This can be used to create a different ChatGPT flavor for every client.
Whereas today, agencies depend on their people to remember how each client likes to get their reports, GPT can be trained to remember these preferences.
Things like how well they know PPC, what jargon they tend to use at their company, and even what the year’s strategic initiatives are.
Then, the LLM can word the summary in a way that resonates with the reader and even explain how the search marketing campaign’s results are key to the company’s strategic objectives for the year.
2. Interactive Dashboards For Real-Time Transparency
AI-driven dashboards provide live, customizable views of campaign performance. Stakeholders can explore data interactively, filtering by date ranges, platforms, or key performance indicators (KPIs), reducing the need for frequent manual report updates.
And while dashboards have been around for a long time, AI can be used to quickly highlight the most salient insights.
For example, AMC lets marketers use AI to generate SQL to explore the data by using natural language.
At my company, Optmyzr, we deployed Sidekick, which can instantly answer questions about data in any account, for example, the biggest optimization opportunities or wins in the last month.
Before AI, these insights might have remained hidden in the data.
Actionable Tips:
Set up custom GPTs for every client you work with.
Implement reporting tools that use natural language to explore the data.
Conclusion: From Reporting To Strategic Decision-Making With Generative AI
Generative AI has redefined PPC reporting, transforming a once fragmented and time-consuming process into a streamlined, insight-driven workflow.
It doesn’t just automate data collection and report generation; it also surfaces hidden trends, correlations, and anomalies that might otherwise go unnoticed.
This enables marketers to make smarter, faster, and more strategic decisions based on real-time insights.
With AI-driven tools, marketers can see beyond surface-level metrics, discovering patterns and opportunities that traditional reporting might take hours or days to uncover.
This improved understanding of performance empowers teams to refine budget allocation, creative strategy, and campaign targeting more effectively, leading to more substantial outcomes and greater profitability.
The conclusion is simple. With Generative AI, PPC managers have more complete data, leading to better insights and better decisions – all of which can be shared more meaningfully with all involved stakeholders.
This post was sponsored by Unbounce. The opinions expressed in this article are the sponsor’s own.
Want to increase sign-ups, sales, or demo requests from your landing page?
How can you ensure your landing page is optimized for conversions?
Landing pages can make or break your conversions.
A well-designed landing page doesn’t just look good; it also seamlessly guides visitors toward action, such as signing up, purchasing, or booking a demo.
A high-performing landing page should align with your goals:
Capturing leads.
Driving sales.
Promoting an event.
The best landing page templates are designed with conversion in mind, featuring strategic layouts, persuasive copy, and clear calls to action.
So, let’s look at a few top-performing landing page examples to learn about why they work and how you should implement them.
1 & 2. FreshGoods & Radiant Yoga Studio: Great For A Clear & Compelling Unique Selling Point
The secret to beating the competition is positioning your brand so you’re the only one in your specific space.
These conversion-optimized landing page templates effectively highlight a USP throughout the design.
A clear and bold headline that immediately communicates the core benefit.
The supporting subheadline allows brands to reinforce the core USP message by expanding on the offer in a way that adds clarity without overwhelming visitors.
The strategic use of whitespace and strong typography ensures that the USP remains the focal point, making it easy for visitors to grasp the value of the offer at a glance.
How To Recreate These Landing Pages
Step 1: Define Your Unique Selling Proposition
A strong USP makes visitors feel like they’ve found exactly what they need. Instead of blending in with competitors, it positions your brand as the only choice.
Ask yourself: What is the one reason customers should choose you over others?
Example: FreshGoods & Radiant Yoga Studio’s landing pages showcase a crystal-clear UVP in their messaging and design.
Step 2: Craft a Compelling Headline & Supporting Headline
Your headline is your first impression, so you have to make it count. The supporting headline expands on that core message.
Best Practices:
Be specific: Instead of “The Best Marketing Tool,” try “Turn Clicks into Customers with AI-Powered Marketing in Minutes.”
Reinforce value: “No coding, no guesswork. Just smarter campaigns that drive real revenue.”
Step 3: Address Concerns with Reinforcing & Closing Statements
A reinforcing statement builds trust (“Trusted by over 10,000 businesses…”).
A closing statement eliminates hesitation (“Every second you wait is a sale you’re losing. Start your free trial now.”)
3 & 4. Vita Health & Orbit SaaS: Great For Hero Images & Visual Storytelling
Before visitors read a single word, visuals will capture their attention and convey meaning.
A strong hero image isn’t just decoration, it sets the tone, builds trust, and instantly reinforces your message. The right imagery makes your offer feel more tangible, relatable, and desirable.
A landing page’s imagery is a strategic tool that helps communicate your offer, build trust, and nudge visitors toward conversion. Choose visuals that don’t just look good but work hard to sell.
A well-chosen visual:
Supports the UVP.
Evokes an emotion that drives action
Showcases the product, service, or outcome in action
Makes the page feel polished, professional, and credible
In addition to the visual, the full landing page benefits from:
Strong hero image placement
An opportunity to reinforce the messaging conveyed with the hero image throughout the page
White space highlights supporting visuals
Visual hierarchy guides site visitors down the page to the parts that matter.
How To Recreate These Landing Pages
Step 1: Choose the Right Hero Image
Before visitors read a word, visuals capture attention. A great hero image should:
Support the USP
Evoke emotion & drive action
Showcase the product, service, or outcome
Step 2: Guide the Visitor’s Eye
Strategic use of visuals can nudge visitors toward your CTA:
Eye gaze: People follow where others are looking in an image.
Angles & positioning: Lines or arrows subtly direct attention to the CTA.
Contrast & color: Key elements should stand out.
Step 3: Reinforce Messaging with Supporting Imagery
Don’t rely on just one image. Use:
Icons & illustrations
Graphs & charts
Customer photos & testimonials
Short videos or GIFs
Bonus Tip:
Use A/B testing to find the ingredients for maximum impact.
The right image can make or break conversions, so test different options. Some images resonate better with your audience, drive more engagement, or feel more aligned with your brand.
Some elements to test include:
People vs. product-focused visuals.
Static images vs. motion (GIFs or videos).
Close-ups vs. wider perspective shots.
Different background colors or lighting.
5 & 6. Serene Vista & Digital Foundry: Great For Clearly Conveying Benefits
Visitors specifically care about what it does for them.
That’s why benefits should take center stage on a conversion-optimized landing page, not just a list of features.
The headshots paired with the social proof enhance trustworthiness and make a connection with site visitors because they can see themselves in the experiences being described.
The rounded shape and contrasting colors make the social proof stand out.
Located near the point of conversion.
How To Create This Landing Page
Step 1: Choose the Right Type of Social Proof
Customer testimonials & reviews
Case studies & success stories
Logos of recognizable brands
Ratings & review scores
Media mentions & awards
Step 2: Strategically Place Social Proof
Near the CTA: Reinforces trust before action.
Midway down the page: Nudges hesitant visitors.
In the hero section: Puts endorsements front and center.
9 & 10. Livewell Lifestyle & Inner Handyman: Great For Turning Interest Into Conversions With Calls To Action
A landing page without a strong CTA is like a roadmap without a destination.
Your CTA is the single most important element that tells visitors what to do next.
And if it’s unclear, compelling, and easy to find, you’ll lose conversions.
A compelling CTA is a combination of copy, design, and placement that removes hesitation and drives action.
CTAs can be customized to stand out and get attention
CTA sizing and positioning make them clear focal points despite having multiple elements on the page. It ensures you get the most conversion power in every pixel
The CTA buttons are placed where it matters throughout the page, making sure the page attempts the conversion when and where it matters most
How To Recreate These Landing Pages
Step 1: Craft a Clear, Compelling CTA
A high-converting CTA should be:
Action-oriented: “Start Growing Today” vs. “Submit”
Benefit-driven: “Unlock Exclusive Access” vs. “Sign Up”
Urgent (if appropriate): “Claim Your Spot Today”
Step 2: CTA Placement for Maximum Impact
Above the fold: First CTA visible immediately.
After key information: CTA follows value explanation.
Near social proof or benefits: Reinforces trust.
At the end of the page: Captures hesitant visitors.
Step 3: CTA Design That Stands Out
Color contrast: The CTA should pop from the background.
Size & positioning: Large enough to be noticeable but not overwhelming.
Whitespace & directional cues: Ensures the CTA is the focal point.
Bonus Tip:
A/B test your CTAs for better conversions.
CTAs aren’t one-size-fits-all. Even small tweaks can make a huge impact on conversions, so A/B testing different variations is essential:
Wording – Try “Get Started” vs. “Try It Free”
Color – A bold button color vs. a softer, branded one
Placement – Above the fold vs. midway down the page
Size and shape – Larger buttons vs. compact ones
Personalization – “Start My Free Trial” vs. “Start Your Free Trial”
Build High-Converting Landing Pages Faster
A great landing page isn’t just about design.
It’s about strategy.
Every element, from your USP and hero images to your social proof and CTAs, is critical in guiding visitors toward conversion. When these elements work together, your landing page drives action.
But building a high-converting landing page from scratch can be time-consuming and complex. That’s why using proven, conversion-optimized templates can give you a head start.
With Unbounce, you get access to 100+ professionally designed landing page templates built for maximum conversions. Whether capturing leads, promoting a product, or running a campaign, these templates help you launch faster, test smarter, and convert better—without needing a developer.
Ready to build an optimized landing page that converts?
Explore Unbounce’s best-performing templates and start optimizing today!
Google is making big moves in 2025, and unsurprisingly, AI is at the heart of it all.
In a recent update, Vidhya Srinivasan, Google’s VP and GM of Ads and Commerce, outlined the company’s top priorities for the coming year.
From AI-powered ad experiences to deeper integrations with YouTube and Google Shopping, these changes signal a clear direction: more automation, more personalization, and a stronger push for immersive ad formats.
Here’s a breakdown of what’s coming and how brands can prepare.
Google’s 2025 Ad Priorities
In Srinivasan’s letter to the industry, she summed up Google’s main priorities into these categories:
AI and personalization
YouTube’s engaged audiences
New ways to search
AI-Driven Personalization and Shopping Experiences
AI isn’t just a buzzword for Google—it’s the backbone of its advertising strategy. Srinivasan emphasized that AI will play a larger role in shaping ad creatives, optimizing bidding strategies, and curating shopping experiences tailored to individual users.
With over a billion shopping activities happening daily on Google, the company is investing heavily in AI-powered product discovery.
Expect to see enhanced AI-generated visuals, automated ad variations, and an improved ability to match users with products based on intent rather than just search keywords.
The revamped Google Shopping experience will feature AI-powered recommendations, immersive 3D product spins, and new ad placements that seamlessly blend into organic search experiences.
YouTube and Search: A Shift to More Visual, Interactive Ads
YouTube is becoming even more central to Google’s ad strategy, especially as younger audiences rely on creators for product recommendations.
Srinivasan noted that Google is working to make ads more interactive and non-disruptive, allowing users to explore products without leaving their video experience.
On the search side, Google is expanding AI-powered search capabilities with tools like AI Overviews and Circle to Search. These innovations will change how users find and engage with ads.
Advertisers will need to rethink their strategies beyond just bidding on keywords—visual and interactive ad formats will become key to capturing attention.
How Advertisers Should Prepare
Staying ahead in PPC in 2025 means adapting to AI-driven changes now.
Google’s changing ad landscape will reward those who embrace automation, optimize creative strategies, and rethink audience targeted.
If you’re not sure where to start, these three components would be a great foundation to shift your PPC strategy.
#1: Shift Toward AI-Optimized Creative
With AI taking a bigger role in ad creation, advertisers need to start testing AI-generated assets now.
Google’s AI tools will allow for automatic variations of images, headlines, and ad copy, making creative testing more efficient.
Brands should focus on providing high-quality inputs—strong branding, clear messaging, and compelling visuals—to ensure AI-generated outputs align with their goals.
#2: Rethink Shopping and Video Strategies
E-commerce brands should lean into AI-powered shopping experiences, ensuring their product feeds are optimized with detailed descriptions, high-resolution images, and accurate inventory data.
With YouTube becoming an even bigger shopping destination, brands should explore shoppable video ads and creator partnerships to drive engagement.
#3: Prepare for a Post-Keyword Ad Landscape
As search evolves, traditional keyword-based targeting will matter less. Instead, audience intent and AI-driven placements will take center stage.
Advertisers should start leveraging first-party data, testing Performance Max campaigns, and using Google’s audience insights to reach the right customers in a more predictive, automated way.
Final Thoughts
Google’s 2025 ad strategy is all about AI, personalization, and more immersive ad experiences.
Advertisers who rely solely on manual optimizations or traditional search strategies may find themselves falling behind.
Now is the time to experiment with AI-powered creative, embrace new ad formats, and rethink how to engage audiences in a world where discovery is just as important as search.
Google Shopping isn’t just about bidding and budget management – it’s about feeding Google the best possible data.
Unlike traditional search ads, where keywords dictate targeting, Shopping campaigns rely on your product feed. The quality, accuracy, and completeness of your product data determine how often and where your ads appear.
A well-optimized feed improves impressions, click-through rates (CTR), and return on ad spend (ROAS).
On the other hand, a neglected feed leads to wasted ad spend, disapproved listings, and poor performance.
Let’s dive into 10 proven ways to optimize your Google Shopping product feed for maximum performance.
1. Perfect Your Product Titles To Improve Rankings And CTR
Your product title is arguably the most critical field in your feed. It directly influences where and how your ad appears in search results.
A well-structured title increases visibility, while a vague or poorly formatted one can bury your product in a sea of competitors.
Best Practices For Writing Effective Product Titles
Front-load the most important details. Google prioritizes the first 70 characters, so key attributes should come first.
Follow a structured format based on your industry. A few examples may include:
Apparel: Brand + Product Name + Product Type + Color + Size.
Electronics: Brand + Product Type + Size + Color + Carrier.
Home & Garden: Brand + Product Type + Feature + {Other Attractive Feature}.
Use descriptive but concise language. Don’t add fluff like “Best Price” or “High-Quality.”
Avoid excessive keyword stuffing. Google may view it as spammy and hurt performance.
Why does this matter? A well-optimized title ensures your product appears in the right searches, increasing relevance and CTR.
2. Write Product Descriptions That Inform And Convert
While product descriptions don’t have as much direct impact on rankings as titles, they still play a crucial role in providing context to Google – and persuading shoppers to convert.
Think of your description as a sales pitch. It should highlight key features, answer common questions, and differentiate your product from competitors.
What To Include In Your Product Description
Essential product details: Size, color, material, features, and compatibility.
Unique selling points (USPs): Why should someone buy from you instead of a competitor?
Use cases: Help shoppers visualize how the product fits into their lives.
Avoid manufacturer descriptions: Rewrite in your own words to add value.
Here’s an example of what not to do:
“This is a high-quality vacuum with advanced suction power.”
Using the tips above, a proper description for a vacuum could read like this:
“The Dyson V15 Detect uses laser dust detection and HEPA filtration, capturing 99.99% of particles for a deep clean. With a 60-minute runtime, it’s ideal for large homes.”
So, why do descriptions matter? It’s the little details that make the biggest differences.
A compelling description not only helps Google categorize your product better, but also increases conversions.
3. Use High-Quality, Compliant Product Images
Images are often the first thing shoppers notice, and low-quality visuals can hurt engagement.
Google also has strict guidelines, and violating them can lead to product disapproval.
Image Optimization Tips
Use high-resolution images (at least 800 x 800 pixels) for clarity and professionalism.
Ensure images accurately depict theproduct – no misleading visuals.
Avoid promotional overlays, text, or watermarks: Google may reject these.
Use multiple images if possible: Include lifestyle shots to showcase real-world use.
For example, if you sell furniture, provide close-up images of textures and finishes. For fashion items, include front, back, and close-up shots to give shoppers a better view.
Better images can help improve CTR, reduce bounce rates on product detail pages, and ultimately drive more conversions.
4. Assign The Most Specific Google Product Category
Google assigns predefined categories to products, and selecting the most accurate one improves your ad’s relevance.
Many advertisers default to broad categories, potentially missing out on better placements.
Here are a few tips on how to choose the right category:
Review the full Google Product Taxonomy regularly. You can find it updated regularly here.
Regularly update your category selections. Because Google’s taxonomy evolves, refining your choices can improve campaign performance over time.
The Google Product Category is an underestimated part of your Google Shopping product feed. The correct category ensures your product appears in relevant searches and prevents misplacements.
5. Utilize The Product Type Attribute For Better Segmentation
Unlike Google’s predefined Product Category, the Product Type attribute is completely customizable.
It’s an opportunity to refine targeting further and structure your campaigns more effectively.
How To Use Product Type Effectively
Use detailed, hierarchical labels whenever possible. For example: “Electronics > Laptops > Gaming Laptops.”
Segment by product performance. For example, separating high vs. low-margin items.
Use it for bidding strategies! You can adjust bids by product type for more control. Just remember that bidding strategies are set at the campaign level, so this would make more sense if your feed has very differently priced or wider margins for certain product categories.
Remember, a well-structured product type attribute can help improve reporting, targeting, and even bid management when done right.
A common reason for disapproval is mismatched pricing between your website and Google Shopping feed.
If shoppers see one price on an ad and another at checkout, you risk losing trust – and conversions along the way.
Below are a few ways you can ensure pricing and availability are (almost) always correct:
Enable automated feed updates via Google’s Content API or scheduled fetches.
Check Google Merchant Center’s Diagnostics regularly for mismatches.
If you run flash sales or limited-time discounts, ensure your feed updates accordingly.
7. Leverage GTINs And MPNs For Stronger Product Matching
If you’re selling branded products, make sure to include Global Trade Item Numbers (GTINs) and Manufacturer Part Numbers (MPNs). Including these helps Google match your product more accurately.
Some key benefits of providing these attributes to your Google Shopping feed include:
Improved ad placement in Google Shopping and free product listings.
Greater visibility in comparison shopping results.
Increased likelihood of appearing in Google’s Buy on Google listings.
Again, you may think these product feed attributes may not be necessary, but better product matching means more impressions and, ultimately, more conversions.
8. Use Custom Labels To Refine Bidding Strategies
Custom labels help segment products based on a number of items, like performance, price, or promotions.
Here are a few examples of how you can use custom labels:
Profitability Segmentation: Separating high-margin vs. low-margin items makes segmenting your campaign and ad group structure easier.
Seasonal Promotions: “Winter Collection” vs. “Summer Deals.”
Stock Levels: Best-sellers vs. clearance items.
Why do custom labels matter in Google Shopping? Better segmentation can lead to more cost-efficient results without lacking conversion volume.
9. Optimize Your Feed For Query-Level Performance
Once you’ve nailed the fundamentals, the next step is optimizing your feed based on actual search queries and performance data.
Instead of treating your feed as a static dataset, you can dynamically adjust product attributes to improve alignment with high-converting queries.
How To Use Query-Level Optimization:
First, start by analyzing your high-performing search terms. Navigate to the search terms report to identify which queries drive the most conversions.
Now, compare those queries with your current product titles and descriptions. Do they match?
If a top-converting term isn’t in your title, update your feed to include it for better alignment.
If you want to take this optimization to the next level, try creating feed rules for automation.
To do this, navigate to “Feed Rules” in Google Merchant Center to set up a logic to append high-performing keywords to titles dynamically.
For example, if a query like “wireless noise-canceling headphones” converts well but your product title only says “Sony WH-1000XM5 Headphones,” a rule can automatically update the title to something like “Sony WH-1000XM5 Wireless Noise-Canceling Headphones.”
This technique ensures your product titles stay relevant without manual updates.
10. Use First-Party Data To Enhance Your Product Feed For Better Personalization
Many advertisers focus solely on optimizing their product feed for Google’s algorithm, but what if you optimized your feed based on your own customer data?
For advertisers managing large Shopping campaigns, leveraging first-party data (like customer purchase behavior, loyalty data, and audience segmentation) can significantly improve feed relevance and drive higher conversion rates.
How To Use First-Party Data To Improve Your Google Shopping Feed
One way to do this is to segment your product feed by buyer intent.
If you have access to customer behavior data from your website, customer relationship management (CRM), or analytics, you can refine your feed to better match different types of shoppers.
Returning Customers: Highlight products frequently purchased by loyal customers by assigning a custom label like “best_seller_loyalty.”
First-Time Shoppers: Adjust product descriptions or titles to emphasize best-sellers or high-converting entry-level products. Try adding a custom label like “high_first_time_purchase_rate.”
High-Value Customers: If certain products have higher purchase frequency among repeat buyers, ensure these have optimized titles, more detailed descriptions, and premium images in your feed.
Secondly, you can set up exclusive offers in the feed if you use loyalty programs or subscriber discounts.
For example, a cosmetics brand sees that loyal customers frequently buy three packs of foundation instead of single bottles.
Instead of just relying on campaign bidding, they optimize the feed by ensuring these multi-packs are included and promoted with proper product titles, descriptions, and subscriber pricing.
Currently, the loyalty feature for Google Shopping is available in the United States and Australia.
Your Product Feed Is The Competitive Edge In Google Shopping
Going beyond traditional feed optimization is key to staying ahead in Google Shopping.
Strategies like query-based feed enhancements and audience-driven bidding can elevate Shopping campaigns from just good to highly profitable and efficient.
By continuously refining how Google understands and matches your products to real shoppers, you gain an edge over competitors still relying on static feeds and generic bidding strategies.
If you’re running high-budget Google Shopping campaigns, it’s worth testing these advanced tactics and letting Google’s automation work smarter, not harder.
